The proliferation of connected vehicles in the Internet of Vehicles (IoV) ecosystem has introduced new security challenges, particularly in the context of internal network attacks. Traditional public key infrastructure (PKI) technologies are no longer sufficient to ensure secure communication within a network that experiences dynamic topology changes and high vehicle density. In response, there is a growing need for a lightweight misbehavior detection framework that offers fast computation and minimal space complexity. This paper presents a novel approach using continuous-time recurrent neural networks for detecting misbehavior in the IoV and assesses their performance against the Vehicular Reference Misbehavior (VeReMi) extension dataset. We compare two recently introduced models—the liquid time-constant (LTC) network and the closed-form continuous-time (CFC) neural network—with the established convolutional neural network-long short-term memory (CNN-LSTM) model. The results indicate that continuous-time neural networks marginally outperform CNN-LSTM on evaluation metrics. Despite LTC and CFC having significantly fewer parameters, making them less complex and more space-efficient than CNN-LSTM, the latter proves to be more time-efficient. Therefore, a careful balance between runtime cost and space complexity must be considered when deploying lightweight neural networks in practical applications.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Lightweight framework for misbehavior detection in internet of vehicles


    Beteiligte:
    Yujing Gong (Autor:in) / Bin-Jie Hu (Autor:in)


    Erscheinungsdatum :

    2025




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    MISBEHAVIOR DETECTION AND INTERVENTION

    ADITHTHAN ARUN / ISLAM MD MHAFUZUL / PERANANDAM PRAKASH M et al. | Europäisches Patentamt | 2025

    Freier Zugriff

    Simulation Framework of Misbehavior Detection and Mitigation for Collective Perception Services

    Zhang, Jiahao / Jemaa, Ines Ben / Nashashibi, Fawzi | IEEE | 2024


    MISBEHAVIOR DETECTION IN AUTONOMOUS DRIVING COMMUNICATIONS

    YANG LIUYANG LILY / SASTRY MANOJ R / LIU XIRUO et al. | Europäisches Patentamt | 2020

    Freier Zugriff

    Misbehavior detection in autonomous driving communications

    YANG LIUYANG LILY / SASTRY MANOJ R / LIU XIRUO et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    CONTEXT-ADAPTIVE RSSI-BASED MISBEHAVIOR DETECTION

    CHEN CONG / PETIT JONATHAN / ANSARI MOHAMMAD RAASHID | Europäisches Patentamt | 2022

    Freier Zugriff